You cannot determine cause from the numbers alone. Often the cause is obvious, and often it requires research to find the cause. A lack of correlation can be sufficient to show that a factor believed to be a cause is not actually the cause. For example, it was long and falsely believed that stress caused ulcers. There was no correlation, though, between stress and ulcers, which puzzled medical researchers. Later, it was discovered that ulcers come from infection with Helicobacter pylori.

But a good clue to the fact that a correlation is causal is a high value for n. If a correlation falls apart when more data is gathered and added to the correlation coefficient calculation, then the original correlation is probably accidental. Please remember that I regard an indirect cause and effect to be sufficient to call it causal. When a cause exists, adding more data (increasing the value for n) will continue the correlation.

Bobbo is correct in his statement about evolution and cause. But evolution is the process that generates causes. There are many Fibonacci series in nature. For example, the scales on a pine cone follow that series, and the cause is the fact that this series permits the scales to be more close packed, thus efficient. Evolution drives the process but the cause of the series is efficiency of packing.

As someone who has been keenly interested in biology all my life, I see correlations in nature that are causal. Accidental correlations also exist but are generally pretty damn unimpressive, and fall apart with more data.

It seems we had to wade through a plethora of semantic issues to find out that we both agree on that point, but it seems our cooperative efforts have succeeded.

Lance Kennedy wrote:Often the cause is obvious,

Yes, because of other information you already have, over and above the correlation numbers.

Lance Kennedy wrote:and often it requires research to find the cause. A lack of correlation can be sufficient to show that a factor believed to be a cause is not actually the cause. For example, it was long and falsely believed that stress caused ulcers. There was no correlation, though, between stress and ulcers, which puzzled medical researchers. Later, it was discovered that ulcers come from infection with Helicobacter pylori.

But a good clue to the fact that a correlation is causal is a high value for n. If a correlation falls apart when more data is gathered and added to the correlation coefficient calculation, then the original correlation is probably accidental. Please remember that I regard an indirect cause and effect to be sufficient to call it causal. When a cause exists, adding more data (increasing the value for n) will continue the correlation.

Bobbo is correct in his statement about evolution and cause. But evolution is the process that generates causes. There are many Fibonacci series in nature. For example, the scales on a pine cone follow that series, and the cause is the fact that this series permits the scales to be more close packed, thus efficient. Evolution drives the process but the cause of the series is efficiency of packing.

As someone who has been keenly interested in biology all my life, I see correlations in nature that are causal. Accidental correlations also exist but are generally pretty damn unimpressive, and fall apart with more data.

It seems we are in general agreement here.

I chose Fibonacci examples because I assumed you would be familiar with those and would need no further explanation from me.

Accidental correlations exist but are mostly pretty damn unimpressive. Yes. Very much uninteresting, except perhaps as examples of "accidental" correlations. But they do exist, was my point.

The other point I was making, which you have not (yet) addressed, but I now infer you agree with, is the following:

If A and B have a strong correlation, and there is a third factor E which causes both, you can rightly say there is a causal relation between A and E, even though there might not be a correlation between A and E. And if E does not even have a list of numbers, then it is not possible to compute a correlation.

That goes to my point that not all causal relations have a correlation.

Furthermore, if E causes A, and F causes B, you can rightly say there are causal relations there, even though there might not be a correlation between E and F. It might be the case that E and F are related in some other way, even if that relationship is not (technically) a correlation.

I have never suggested that cause can be determined from numbers alone. But the probability that a cause lies behind a correlation can be inferred if r (the correlation coefficient) and n are both high. Accidental correlations are unimpressive, mainly because most of them have a low value for n. Thus the reliability (t) is low.

Most strong correlations are from a cause and effect relationship.

If E causes A, there will be a correlation between those variables.If F causes B, there will be a correlation between them.But if E and F are not correlated, then why should A and B be correlated? If there is a correlation between E and F and it is neither accidental or causal, then what is it ?

Let me go back to a much, much earlier point. I told you way, way back that a correlation does not prove causation, but merely suggests it. You did not like that. But since you have defined "imply " as "leading to a conclusion " . In maths, 'leading to a conclusion ' is so close to 'proves ', that I can go back to my earlier statement, and say that a correlation does not prove causation , but it does suggest causation. Since the word "suggest " is not the same as proves, then the existence of some accidental correlations does not obviate this statement.

To make my meaning clear, let me say that in this context, the word suggests means "increases the probability of ".

I have never suggested that cause can be determined from numbers alone.

OK, thanks for clarifying that. Perhaps the quibbles about semantics got in the way of reaching that mutual understanding.

Lance Kennedy wrote: But the probability that a cause lies behind a correlation can be inferred if r (the correlation coefficient) and n are both high.

That claim remains to be proven.

And by "proven" I mean sufficiently supported by credible evidence, which by your standards would be a journal paper.

Lance Kennedy wrote: Accidental correlations are unimpressive, mainly because most of them have a low value for n. Thus the reliability (t) is low.

Yet another claim that remains to be proven. How do you know "most of them" have a low n?

Lance Kennedy wrote:Most strong correlations are from a cause and effect relationship.

Yet another claim that remains to be proven. How do you know "most of them" have a cause and effect relation?

Lance Kennedy wrote:If E causes A, there will be a correlation between those variables.

Sometimes yes, sometimes no.

You have already agreed that there is no correlation between "evolution" and the growth rate of a nautilus shell, even though there is a cause and effect relation.

Lance Kennedy wrote:If F causes B, there will be a correlation between them.

But if E and F are not correlated, then why should A and B be correlated?

It is already a given that A and B are correlated. That's the starting premise which we assume to be true for the sake of this discussion. The question then is are E and F correlated? Sometimes yes, sometimes no. And I already explained how it is possible that E and F are not correlated. It could be the case that they have no list of numbers from which to compute a correlation.

Lance Kennedy wrote:Let me go back to a much, much earlier point. I told you way, way back that a correlation does not prove causation, but merely suggests it. You did not like that. But since you have defined "imply " as "leading to a conclusion " . In maths, 'leading to a conclusion ' is so close to 'proves ', that I can go back to my earlier statement, and say that a correlation does not prove causation , but it does suggest causation. Since the word "suggest " is not the same as proves, then the existence of some accidental correlations does not obviate this statement.

To make my meaning clear, let me say that in this context, the word suggests means "increases the probability of ".

Using your definitions, your claim that "correlation suggests causation" remains to be proven*.

The scientific community does not agree with you.

And by "proven" I mean sufficiently supported by credible evidence, which by your standards would be a journal paper.

Please do not use the word 'proven ' when asking for evidence. You know perfectly well that there is no proof in science. Only evidence.

Accidental correlations normally have low values for n, meaning less than 12. You can get this evidence easily by looking at the lists of accidental correlations published on the internet. I have done this. You can also do it very easily.

The scientific community does not disagree with me that correlation increases the probability of causation. They do not disagree with the slogan that correlation does not lead to the conclusion of causation. But that is quite different to a change in probability. The attitudes of the scientific community is shown by the massive amount of work they do to find correlations. This is especially true in some branches of science, like ecology and epidemiology.

Your statement about E and F not being correlated because they do not have a list of numbers, is possibly correct, but that is not a denial of the principle. It is just that for some variables, scientists do not yet know how to quantify. For example, if I were a psychologist wanting to find a correlation between levels of depression and seratonin quantities in the brain, I might struggle to find a way to quantify, and put numbers in for levels of depression . But that does not mean the correlation is not there. It just reflects human limitations.

And by "proven" I mean sufficiently supported by credible evidence, which by your standards would be a journal paper.

Lance Kennedy wrote:You can get this evidence easily by looking at the lists of accidental correlations published on the internet. I have done this. You can also do it very easily.

If you are claiming that "accidental" correlations do not exist if they are not published on the internet, then that too is a claim that remains to be proven. Where's the journal paper that supports your claim?

Lance Kennedy wrote:The scientific community does not disagree with me that correlation increases the probability of causation.

That claim remains to be proven. Where's the journal paper that supports your claim?

Lance Kennedy wrote:Your statement about E and F not being correlated because they do not have a list of numbers, is possibly correct, but that is not a denial of the principle. It is just that for some variables, scientists do not yet know how to quantify. For example, if I were a psychologist wanting to find a correlation between levels of depression and seratonin quantities in the brain, I might struggle to find a way to quantify, and put numbers in for levels of depression . But that does not mean the correlation is not there. It just reflects human limitations.

If you don't have the numbers, then there is no way to compute the correlation, and thus by definition, there is in fact no correlation.

I am currently reading Bill Nye's latest book, and I think he represents the scientific community pretty well. Let me quote from his book.

" In statistics, phi is a measure of the correlation between two separate factors, and so it is a crucial measure for distinguishing chance events from cause and effect in scientific experiments. Stick that in your back pocket. "

And yes, he did say that bit about the back pocket. I would not have said that, since I am more oriented to ecology (many years ago, my dissertation was on the ecology of a small group of whelks. Hardly Earth shattering stuff, but still interesting.) I tend to see correlations as revealing cause and effect relationships in nature rather than in experiments. But what the heck

Lance Kennedy wrote:I am currently reading Bill Nye's latest book, and I think he represents the scientific community pretty well. Let me quote from his book.

" In statistics, phi is a measure of the correlation between two separate factors, and so it is a crucial measure for distinguishing chance events from cause and effect in scientific experiments. Stick that in your back pocket. "

And yes, he did say that bit about the back pocket. I would not have said that, since I am more oriented to ecology (many years ago, my dissertation was on the ecology of a small group of whelks. Hardly Earth shattering stuff, but still interesting.) I tend to see correlations as revealing cause and effect relationships in nature rather than in experiments. But what the heck

I too have that book. I just now emailed him and asked if he would clarify what he meant by that. Stay tuned . . .

In any case, that book does not meet your standard of evidence, since it is not in a peer-reviewed and reputable scientific journal.

I do not need a peer reviewed research article for this subject. That is your demand, not mine. I have the experience working with data to know what is used by scientists as practical people. It appears Bill Nye is the same.

Neither of us is claiming accidental correlations do not happen. My claim is that a good correlation (high value for t) raises the probability of causation. That is what practical researchers use correlations for. Not proof, but an indication.

If and when you get a reply from Bill Nye, would you please post not just his reply, but also your question to him. The way a question is worded often influences the answer, and I would need that to understand his answer.

Lance Kennedy wrote:I do not need a peer reviewed research article for this subject. That is your demand, not mine.

You have required journal papers from others who make claims, so I am merely applying your own standard to your claims. Seems fair to me.

Lance Kennedy wrote:If and when you get a reply from Bill Nye, would you please post not just his reply, but also your question to him. The way a question is worded often influences the answer, and I would need that to understand his answer.

I agree. Except I forgot to ask his permission to post his reply. I'll send a followup email, correcting that oversight.

Re journal papers.I do not think this is comparing apples with apples. Something as vital as visiting aliens would have to be studied by scientists and published. But the percentage of correlations that are causal is probably not quantifiable, due to sheer numbers of examples.

As a scuba diver, I am very aware of cause and effect based correlations with depth. There must be literally millions of causation correlations between marine organisms and various ocean depths. They are all based on cause and effect. There are millions more related to distances up mountains, distances from water sources, degrees of exposure to light, distances from heat sources, and so on. The sheer number of causation correlations is mind blowing, and I do not think they would be quantifiable in any meaningful way.

But simply knowing all this gives me a feel for the subject. Frankly, even with computers searching the data, you could not obtain any appreciable fraction of the number of causation correlations as accidental ones. You suggested that one web site had 30,000. I am inclined to bet that most of them were causation via a third variable rather than accidental. But even 30,000 does not form even the tiniest fraction of the correlations available that are based on cause and effect.

Lance Kennedy wrote:. . . Frankly, even with computers searching the data, you could not obtain any appreciable fraction of the number of causation correlations as accidental ones. You suggested that one web site had 30,000. I am inclined to bet that most of them were causation via a third variable rather than accidental. But even 30,000 does not form even the tiniest fraction of the correlations available that are based on cause and effect.

I have already agreed that there are a bigly number of correlations with a causal relation. Way more than 30,000. You don't have to keep arguing that point. I agreed with it a long time ago.

What you have not shown is that K > N. You have merely surmised it.

Reminder:K is the number of strong correlations with a causal relation.N is the number of strong correlations with no causal relation.

Given the total number of possible variables in the universe, if we run a correlation on all possible pairs — and I'm guessing there will be two or three gobzillion pairs to compute — some of them will be strong correlations. You are claiming that of all those strong correlations, most of them will also have a causal relation. Since you have no evidence for that claim beyond mere supposition, I reject it until further notice.

On the other hand, in the subset of strong correlations that you personally know about, I will accept your claim that most of them have some kind of causal relation. However, you are not justified in making any claim about the strong correlations you do not personally know about.

For example:Let Ks be the subset of strong correlations with a causal relation, that you know about personally.Let Ns be the subset of strong correlations with no causal relation, that you know about personally.

Where K > Ks and N > Ns

Then I will accept your claim that Ks > Ns

But that does not prove that K > N, because you admitted you have no knowledge whatsoever how big N is.

But I do not have to discuss all the possible correlations throughout the universe. My concern is only the ones being investigated by scientists here on Earth. I guess you could say there a degree of pre -selection, in that scientists are not likely to be investigating all correlations that might be calculated using random choice methods. Instead, the are looking at topics of interest.

So if we call topics of interest a subset of all possible random correlations, then out of that group, the majority of strong correlations will be causal.

Lance Kennedy wrote:. . . My concern is only the ones being investigated by scientists here on Earth. I guess you could say there a degree of pre -selection, in that scientists are not likely to be investigating all correlations that might be calculated using random choice methods. Instead, the are looking at topics of interest.

So if we call topics of interest a subset of all possible random correlations, then out of that group, the majority of strong correlations will be causal.